cs448f: image processing for photography and vision
DESCRIPTION
CS448f: Image Processing For Photography and Vision. Blending and Pyramids. Blending. We’ve aligned our images. What now? Averaging Weighted averaging min/max/median. Noise reduction by Averaging. 2 Shots. 4 Shots. 8 Shots. 16 Shots. Noise Reduction by Averaging. - PowerPoint PPT PresentationTRANSCRIPT
CS448f: Image Processing For Photography and Vision
Blending and Pyramids
Blending
• We’ve aligned our images. What now?• Averaging• Weighted averaging• min/max/median
Noise reduction by Averaging
2 Shots
4 Shots
8 Shots
16 Shots
Noise Reduction by Averaging
• We’re averaging random variables X and Y• Both have variance S2
• Variance of X+Y = 2S2
• Std.Dev. of X+Y = sqrt(2) . S• Std.Dev of (X+Y)/2 = sqrt(2)/2 . S• Ie, every time we take twice as many photos,
we reduce noise by sqrt(2)
Noise Reduction by Averaging
• Average 4 photos: noise gets reduced 2x• Average 8 photos: noise gets reduced 3x• Average 16 photos: noise gets reduced 4x
Noise Reduction by Median
• (demo)
Median v Average
Median v Average
Can we identify the bad pixels?
• They’re unlike their neighbours• Instead of averaging, weighted average – where weight = similarity to neighbours
Weighted Average
Can we identify the bad pixels?
• They’re unlike their neighbours• Instead of averaging, weighted average – where weight = similarity to neighbours
• Favors blurriness
Input
Other uses of Median
• Removing Transient Occluders• (live demo)• (Gates demo)• (surf demo)
Panorama Stitching
Panorama Stitching
Panorama Stitching
Panorama Stitching
Panorama Stitching
Multiple Exposure Fusion
Multiple Exposure Fusion
Multiple Exposure Fusion
Multiple Exposure Fusion
Multiple Exposure Fusion
Multiple Exposure Fusion
Multiple Exposure Fusion
Multiple Exposure Fusion
Multiple Exposure Fusion
Multiple Exposure Fusion
Multiple Exposure Fusion
Focus Fusion
Focus Fusion
Focus Fusion
Focus Fusion
Focus Fusion
Pyramids
• We’ve been breaking images into two terms for a variety of apps– Coarse + Fine
• More generally we can break it into many terms:– Very coarse + finer + finer ... + finest.
Pyramids• We can do this by blurring more and more:
Pyramids• And then (optionally) taking differences
--
Pyramids• The coarse layers can be stored at low res.
GaussianPyramid
LaplacianPyramid
Pyramids• How much memory does this use?
Pyramid Uses:
• Sampling arbitrarily sized Gaussians
• Equalizing an image– The different levels represent different frequency ranges– We can scale each frequency level and recombine
• Blending multiple images
Pyramid Blending
• Key Insight:– Coarse structure should blend very slowly
between images (lots of feathering), while fine details should transition more quickly.
• More robust to tricky cases than plain old compositing
Inputs:
Compositing: Hard Mask
Compositing: Soft Mask
Multi-Band Blending
Exposure Fusion• http://research.edm.uhasselt.be/~tmertens/papers/exposure_fusion_reduced.pdf